coefCI

Class: NonLinearModel

Confidence intervals of coefficient estimates of nonlinear regression model

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Syntax

ci = coefCI(mdl) ci = coefCI(mdl,alpha)

Description

ci = coefCI(mdl) returns confidence intervals for the coefficients in mdl.

ci = coefCI(mdl,alpha) returns confidence intervals with confidence level 1 - alpha.

Input Arguments

mdl

Nonlinear regression model, constructed by fitnlm.

alpha

Scalar from 0 to 1, the probability that the confidence interval does not contain the true value.

Default: 0.05

Output Arguments

`ci`	`k`-by-`2` matrix of confidence intervals. The `j`th row of `ci` is the confidence interval of coefficient `j` of `mdl`. The name of coefficient `j` of `mdl` is in `mdl``.CoefNames`.

Examples

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Default Confidence Intervals

Open Live Script

Create a nonlinear model for auto mileage based on the carbig data. Then obtain confidence intervals for the resulting model coefficients.

Load the data and create a nonlinear model.

load carbig
ds = dataset(Horsepower,Weight,MPG);
modelfun = @(b,x)b(1) + b(2)*x(:,1) + ...
    b(3)*x(:,2) + b(4)*x(:,1).*x(:,2);
beta0 = [1 1 1 1];
mdl = fitnlm(ds,modelfun,beta0)

mdl = 
Nonlinear regression model:
    MPG ~ b1 + b2*Horsepower + b3*Weight + b4*Horsepower*Weight

Estimated Coefficients:
           Estimate         SE         tStat       pValue  
          __________    __________    _______    __________

    b1        63.558        2.3429     27.127    1.2343e-91
    b2      -0.25084      0.027279    -9.1952    2.3226e-18
    b3     -0.010772    0.00077381    -13.921    5.1372e-36
    b4    5.3554e-05    6.6491e-06     8.0542    9.9336e-15


Number of observations: 392, Error degrees of freedom: 388
Root Mean Squared Error: 3.93
R-Squared: 0.748,  Adjusted R-Squared 0.746
F-statistic vs. constant model: 385, p-value = 7.26e-116

All the coefficients have extremely small $p$ -values. This means a confidence interval around the coefficients will not contain the point 0, unless the confidence level is very high.

Find 95% confidence intervals for the coefficients of the model.

ci = coefCI(mdl)

ci = 4×2

   58.9515   68.1644
   -0.3045   -0.1972
   -0.0123   -0.0093
    0.0000    0.0001

The confidence interval for b4 seems to contain 0. Examine it in more detail.

ci(4,:)

ans = 1×2
10^-4 ×

    0.4048    0.6663

As expected, the confidence interval does not contain the point 0.

More About

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Confidence Interval

The coefficient confidence intervals provide a measure of precision for regression coefficient estimates.

A 100(1 – α)% confidence interval gives the range that the corresponding regression coefficient will be in with 100(1 – α)% confidence, meaning that 100(1 – α)% of the intervals resulting from repeated experimentation will contain the true value of the coefficient.

The software finds confidence intervals using the Wald method. The 100*(1 – α)% confidence intervals for regression coefficients are

$b_{i} \pm t_{(1 - α / 2, n - p)} S E (b_{i}),$

where b_i is the coefficient estimate, SE(b_i) is the standard error of the coefficient estimate, and t_{(1–α/2,n–p)} is the 100(1 – α/2) percentile of t-distribution with n – p degrees of freedom. n is the number of observations and p is the number of regression coefficients.

Documentation

coefCI

Syntax

Description

Input Arguments

Output Arguments

Examples

Default Confidence Intervals

More About

Confidence Interval

See Also

Topics

Statistics and Machine Learning Toolbox Documentation

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